Typically, ontology reasoning is a process for deriving implicit knowledge from explicitly given ontology knowledge. Further, ontology knowledge in a semantic web is represented by languages, such as Resource Description Framework (RDF), an RDF Schema (RDFS), and Web Ontology Language (OWL). Since ontology technology is a set of RDF triples, rule-based reasoning is a process for deriving a new triple by applying given rules to a set of explicitly given RDF triples.
Recently, as semantic web technology has become widely propagated, various institutions generate ontology knowledge, and thus the scale of ontology knowledge gradually increases. Accordingly, a large-scale ontology reasoning system (hereinafter referred to as a ‘reasoning system’) capable of efficiently storing and reasoning large-capacity RDF triples (hereinafter referred to as ‘triples’) is urgently required.
As a result, in the prior art, a ‘reasoning method using a Rete algorithm’ (hereinafter referred to as a ‘memory reasoning method’) was proposed. This reduces a repeated pattern matching process by efficiently performing pattern matching, but there is a problem in that excessive memory is required. That is, in a reasoning system requiring the processing of more than several billions of triples, both α-memory and β-memory, which are data generated during a procedure for applying reasoning rules to the triples, must have been stored in physical memory (for example, Random Access Memory [RAM]), and thus there is the problematic requirement for large-capacity memory.
A ‘reasoning method using a Database Management System (DBMS) (hereinafter referred to as a ‘DBMS reasoning method’), which has been proposed in the prior art to solve such a problem, is suitable for the storage of large-capacity triples, but the problem of inefficiency in that that DB tables must be repeatedly read and written during a reasoning process is presented.